Next Article in Journal
Dual-Time-Point FDG Uptake Correlates with Prognostic Factors of Invasive Breast Cancer: Clinical Usefulness of Early Delayed Scanning
Previous Article in Journal
Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities
Previous Article in Special Issue
Ovarian Cancer Screening: Lessons about Effectiveness
Article Menu

Article Versions

Export Article

Open AccessArticle
Diagnostics 2019, 9(2), 39; https://doi.org/10.3390/diagnostics9020039

Mining Featured Biomarkers Linked with Epithelial Ovarian Cancer Based on Bioinformatics

1
Department of Endocrinology, J.J. M Medical College, Davanagere, Karnataka 577004, India
2
Department of Obstetrics and Gynecology, J.J. M Medical College, Davanagere, Karnataka 577004, India
3
Department of Pharmaceutics, SET`S College of Pharmacy, Dharwad, Karnataka 580002, India
4
Biostatistics and Bioinformatics,Chanabasava Nilaya, Bharthinagar,Dharwad, Karanataka 580001, India
*
Author to whom correspondence should be addressed.
Received: 24 January 2019 / Revised: 31 March 2019 / Accepted: 5 April 2019 / Published: 9 April 2019
(This article belongs to the Special Issue Ovarian Cancer: Characteristics, Screening, Diagnosis and Treatment)
PDF [2460 KB, uploaded 9 April 2019]

Abstract

: Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors ) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C) , VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC.
Keywords: epithelial ovarian cancer; bioinformatics analysis; differentially-expressed genes; PPI network; survival analysis epithelial ovarian cancer; bioinformatics analysis; differentially-expressed genes; PPI network; survival analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Alur, V.C.; Raju, V.; Vastrad, B.; Vastrad, C. Mining Featured Biomarkers Linked with Epithelial Ovarian Cancer Based on Bioinformatics. Diagnostics 2019, 9, 39.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Diagnostics EISSN 2075-4418 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top